discover_dataflows

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the search scope and return fields, but does not detail search semantics (e.g., case sensitivity, fuzzy matching), pagination behavior, error handling, or potential rate limits. The description gives a basic behavioral overview but lacks depth beyond the obvious read-only search nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the primary action, and contains no filler. Every sentence contributes useful information—what it does, what it returns, and how the output is used.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple search tool with a full input schema and an output schema present, the description is complete. It provides the purpose, the output fields, and the downstream usage context (get_constraints), leaving no critical gaps for an agent to invoke it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 100% of parameters with descriptions (query and limit). The tool description adds the workflow context that the datastructure id is used for get_constraints, but it does not add new meaning to the parameters themselves. Baseline 3 is appropriate given the high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific purpose: 'Search ~4,700 available ISTAT statistical datasets (dataflows) by keywords.' It distinguishes this tool from siblings by focusing on the discovery step and specifying the return fields (dataflow id, Italian name, datastructure id), which directly supports downstream use with get_constraints.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly indicates when to use this tool by stating the output is 'to feed into get_constraints,' providing a clear workflow context. It does not explicitly mention exclusions or alternative tools, but the context is sufficient for an agent to understand this is for discovering dataflows before retrieving constraints.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.